

def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			_validate_point(a__)
			_validate_point(a__)
			if len(a__) != len(a__):
						raise ValueError("""Both points must be in the same n-dimensional space""")

			return float(sum(abs(a - b) for a, b in zip(a__  ,	a__)))


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			if point:
						if isinstance(a__  ,	a__):
									for item in point:
												if not isinstance(a__  ,	(int, float)):
															a_						:						Optional[Any] 							= (
															    """Expected a list of numbers as input, found """
															    f'''{type(a__).__name__}'''
															)
															raise TypeError(a__)
						else:
									a_						:						Optional[Any] 							= f'''Expected a list of numbers as input, found {type(a__).__name__}'''
									raise TypeError(a__)
			else:
						raise ValueError("""Missing an input""")


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			_validate_point(a__)
			_validate_point(a__)
			if len(a__) != len(a__):
						raise ValueError("""Both points must be in the same n-dimensional space""")

			return float(sum(abs(x - y) for x, y in zip(a__  ,	a__)))


if __name__ == "__main__":
	import doctest

	doctest.testmod()





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case				:      int   		=			{
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      int   		=			["""LlamaTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      int   		=			["""LlamaTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Union[str, Any]   		=			[
	    """LlamaForCausalLM""",
	    """LlamaModel""",
	    """LlamaPreTrainedModel""",
	    """LlamaForSequenceClassification""",
	]


if TYPE_CHECKING:
	from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama_fast import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
	import sys

	__snake_case				:      Dict   		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


